la2sna
2022-07-02
EDA LA2
Name: — USN:
Akhilesh Kumar Mishra — 1nt19is010
Prarabdh Joshi — 1nt19is114
The dataset has three attributes Age , Estimated Salary , Purchased. Target value is purchased which is based on the Attributes Age and EStimated salary .
based on the analysis of the graphs conclusion::
if the persons is age is above 40years and the salary is above 80k then the purchase is high i.e 1. (Exceptions are also present).
getwd()## [1] "D:/sna"setwd("D:/sna")getwd()## [1] "D:/sna" df<-read.csv("D:\\sna\\Social_Network_Ads.csv")df## Age EstimatedSalary Purchased
## 1 19 19000 0
## 2 35 20000 0
## 3 26 43000 0
## 4 27 57000 0
## 5 19 76000 0
## 6 27 58000 0
## 7 27 84000 0
## 8 32 150000 1
## 9 25 33000 0
## 10 35 65000 0
## 11 26 80000 0
## 12 26 52000 0
## 13 20 86000 0
## 14 32 18000 0
## 15 18 82000 0
## 16 29 80000 0
## 17 47 25000 1
## 18 45 26000 1
## 19 46 28000 1
## 20 48 29000 1
## 21 45 22000 1
## 22 47 49000 1
## 23 48 41000 1
## 24 45 22000 1
## 25 46 23000 1
## 26 47 20000 1
## 27 49 28000 1
## 28 47 30000 1
## 29 29 43000 0
## 30 31 18000 0
## 31 31 74000 0
## 32 27 137000 1
## 33 21 16000 0
## 34 28 44000 0
## 35 27 90000 0
## 36 35 27000 0
## 37 33 28000 0
## 38 30 49000 0
## 39 26 72000 0
## 40 27 31000 0
## 41 27 17000 0
## 42 33 51000 0
## 43 35 108000 0
## 44 30 15000 0
## 45 28 84000 0
## 46 23 20000 0
## 47 25 79000 0
## 48 27 54000 0
## 49 30 135000 1
## 50 31 89000 0
## 51 24 32000 0
## 52 18 44000 0
## 53 29 83000 0
## 54 35 23000 0
## 55 27 58000 0
## 56 24 55000 0
## 57 23 48000 0
## 58 28 79000 0
## 59 22 18000 0
## 60 32 117000 0
## 61 27 20000 0
## 62 25 87000 0
## 63 23 66000 0
## 64 32 120000 1
## 65 59 83000 0
## 66 24 58000 0
## 67 24 19000 0
## 68 23 82000 0
## 69 22 63000 0
## 70 31 68000 0
## 71 25 80000 0
## 72 24 27000 0
## 73 20 23000 0
## 74 33 113000 0
## 75 32 18000 0
## 76 34 112000 1
## 77 18 52000 0
## 78 22 27000 0
## 79 28 87000 0
## 80 26 17000 0
## 81 30 80000 0
## 82 39 42000 0
## 83 20 49000 0
## 84 35 88000 0
## 85 30 62000 0
## 86 31 118000 1
## 87 24 55000 0
## 88 28 85000 0
## 89 26 81000 0
## 90 35 50000 0
## 91 22 81000 0
## 92 30 116000 0
## 93 26 15000 0
## 94 29 28000 0
## 95 29 83000 0
## 96 35 44000 0
## 97 35 25000 0
## 98 28 123000 1
## 99 35 73000 0
## 100 28 37000 0
## 101 27 88000 0
## 102 28 59000 0
## 103 32 86000 0
## 104 33 149000 1
## 105 19 21000 0
## 106 21 72000 0
## 107 26 35000 0
## 108 27 89000 0
## 109 26 86000 0
## 110 38 80000 0
## 111 39 71000 0
## 112 37 71000 0
## 113 38 61000 0
## 114 37 55000 0
## 115 42 80000 0
## 116 40 57000 0
## 117 35 75000 0
## 118 36 52000 0
## 119 40 59000 0
## 120 41 59000 0
## 121 36 75000 0
## 122 37 72000 0
## 123 40 75000 0
## 124 35 53000 0
## 125 41 51000 0
## 126 39 61000 0
## 127 42 65000 0
## 128 26 32000 0
## 129 30 17000 0
## 130 26 84000 0
## 131 31 58000 0
## 132 33 31000 0
## 133 30 87000 0
## 134 21 68000 0
## 135 28 55000 0
## 136 23 63000 0
## 137 20 82000 0
## 138 30 107000 1
## 139 28 59000 0
## 140 19 25000 0
## 141 19 85000 0
## 142 18 68000 0
## 143 35 59000 0
## 144 30 89000 0
## 145 34 25000 0
## 146 24 89000 0
## 147 27 96000 1
## 148 41 30000 0
## 149 29 61000 0
## 150 20 74000 0
## 151 26 15000 0
## 152 41 45000 0
## 153 31 76000 0
## 154 36 50000 0
## 155 40 47000 0
## 156 31 15000 0
## 157 46 59000 0
## 158 29 75000 0
## 159 26 30000 0
## 160 32 135000 1
## 161 32 100000 1
## 162 25 90000 0
## 163 37 33000 0
## 164 35 38000 0
## 165 33 69000 0
## 166 18 86000 0
## 167 22 55000 0
## 168 35 71000 0
## 169 29 148000 1
## 170 29 47000 0
## 171 21 88000 0
## 172 34 115000 0
## 173 26 118000 0
## 174 34 43000 0
## 175 34 72000 0
## 176 23 28000 0
## 177 35 47000 0
## 178 25 22000 0
## 179 24 23000 0
## 180 31 34000 0
## 181 26 16000 0
## 182 31 71000 0
## 183 32 117000 1
## 184 33 43000 0
## 185 33 60000 0
## 186 31 66000 0
## 187 20 82000 0
## 188 33 41000 0
## 189 35 72000 0
## 190 28 32000 0
## 191 24 84000 0
## 192 19 26000 0
## 193 29 43000 0
## 194 19 70000 0
## 195 28 89000 0
## 196 34 43000 0
## 197 30 79000 0
## 198 20 36000 0
## 199 26 80000 0
## 200 35 22000 0
## 201 35 39000 0
## 202 49 74000 0
## 203 39 134000 1
## 204 41 71000 0
## 205 58 101000 1
## 206 47 47000 0
## 207 55 130000 1
## 208 52 114000 0
## 209 40 142000 1
## 210 46 22000 0
## 211 48 96000 1
## 212 52 150000 1
## 213 59 42000 0
## 214 35 58000 0
## 215 47 43000 0
## 216 60 108000 1
## 217 49 65000 0
## 218 40 78000 0
## 219 46 96000 0
## 220 59 143000 1
## 221 41 80000 0
## 222 35 91000 1
## 223 37 144000 1
## 224 60 102000 1
## 225 35 60000 0
## 226 37 53000 0
## 227 36 126000 1
## 228 56 133000 1
## 229 40 72000 0
## 230 42 80000 1
## 231 35 147000 1
## 232 39 42000 0
## 233 40 107000 1
## 234 49 86000 1
## 235 38 112000 0
## 236 46 79000 1
## 237 40 57000 0
## 238 37 80000 0
## 239 46 82000 0
## 240 53 143000 1
## 241 42 149000 1
## 242 38 59000 0
## 243 50 88000 1
## 244 56 104000 1
## 245 41 72000 0
## 246 51 146000 1
## 247 35 50000 0
## 248 57 122000 1
## 249 41 52000 0
## 250 35 97000 1
## 251 44 39000 0
## 252 37 52000 0
## 253 48 134000 1
## 254 37 146000 1
## 255 50 44000 0
## 256 52 90000 1
## 257 41 72000 0
## 258 40 57000 0
## 259 58 95000 1
## 260 45 131000 1
## 261 35 77000 0
## 262 36 144000 1
## 263 55 125000 1
## 264 35 72000 0
## 265 48 90000 1
## 266 42 108000 1
## 267 40 75000 0
## 268 37 74000 0
## 269 47 144000 1
## 270 40 61000 0
## 271 43 133000 0
## 272 59 76000 1
## 273 60 42000 1
## 274 39 106000 1
## 275 57 26000 1
## 276 57 74000 1
## 277 38 71000 0
## 278 49 88000 1
## 279 52 38000 1
## 280 50 36000 1
## 281 59 88000 1
## 282 35 61000 0
## 283 37 70000 1
## 284 52 21000 1
## 285 48 141000 0
## 286 37 93000 1
## 287 37 62000 0
## 288 48 138000 1
## 289 41 79000 0
## 290 37 78000 1
## 291 39 134000 1
## 292 49 89000 1
## 293 55 39000 1
## 294 37 77000 0
## 295 35 57000 0
## 296 36 63000 0
## 297 42 73000 1
## 298 43 112000 1
## 299 45 79000 0
## 300 46 117000 1
## 301 58 38000 1
## 302 48 74000 1
## 303 37 137000 1
## 304 37 79000 1
## 305 40 60000 0
## 306 42 54000 0
## 307 51 134000 0
## 308 47 113000 1
## 309 36 125000 1
## 310 38 50000 0
## 311 42 70000 0
## 312 39 96000 1
## 313 38 50000 0
## 314 49 141000 1
## 315 39 79000 0
## 316 39 75000 1
## 317 54 104000 1
## 318 35 55000 0
## 319 45 32000 1
## 320 36 60000 0
## 321 52 138000 1
## 322 53 82000 1
## 323 41 52000 0
## 324 48 30000 1
## 325 48 131000 1
## 326 41 60000 0
## 327 41 72000 0
## 328 42 75000 0
## 329 36 118000 1
## 330 47 107000 1
## 331 38 51000 0
## 332 48 119000 1
## 333 42 65000 0
## 334 40 65000 0
## 335 57 60000 1
## 336 36 54000 0
## 337 58 144000 1
## 338 35 79000 0
## 339 38 55000 0
## 340 39 122000 1
## 341 53 104000 1
## 342 35 75000 0
## 343 38 65000 0
## 344 47 51000 1
## 345 47 105000 1
## 346 41 63000 0
## 347 53 72000 1
## 348 54 108000 1
## 349 39 77000 0
## 350 38 61000 0
## 351 38 113000 1
## 352 37 75000 0
## 353 42 90000 1
## 354 37 57000 0
## 355 36 99000 1
## 356 60 34000 1
## 357 54 70000 1
## 358 41 72000 0
## 359 40 71000 1
## 360 42 54000 0
## 361 43 129000 1
## 362 53 34000 1
## 363 47 50000 1
## 364 42 79000 0
## 365 42 104000 1
## 366 59 29000 1
## 367 58 47000 1
## 368 46 88000 1
## 369 38 71000 0
## 370 54 26000 1
## 371 60 46000 1
## 372 60 83000 1
## 373 39 73000 0
## 374 59 130000 1
## 375 37 80000 0
## 376 46 32000 1
## 377 46 74000 0
## 378 42 53000 0
## 379 41 87000 1
## 380 58 23000 1
## 381 42 64000 0
## 382 48 33000 1
## 383 44 139000 1
## 384 49 28000 1
## 385 57 33000 1
## 386 56 60000 1
## 387 49 39000 1
## 388 39 71000 0
## 389 47 34000 1
## 390 48 35000 1
## 391 48 33000 1
## 392 47 23000 1
## 393 45 45000 1
## 394 60 42000 1
## 395 39 59000 0
## 396 46 41000 1
## 397 51 23000 1
## 398 50 20000 1
## 399 36 33000 0
## 400 49 36000 12.Scatter plot
plot(df$Age, df$EstimatedSalary)
scatter plot using ggplot.
library(ggplot2)
ggplot(df, aes(x = Age, y = EstimatedSalary )) +
geom_point()
3.
ggplot(data = NULL, aes(x = df$Age, y = df$EstimatedSalary)) +
geom_point()
4.creating line graph
plot(df$Age, df$EstimatedSalary, type = "l")
- scatter + points graph
plot(df$Age, df$EstimatedSalary, type = "l")
points(df$Age, df$EstimatedSalary)
6. estimated Salary divided by 2 and color of line changed to red
plot(df$Age, df$EstimatedSalary/2, col="red",type="l")
points(df$Age, df$EstimatedSalary, col="red")
7.using gg plot line graph
library(ggplot2)
ggplot(df, aes(x = Age, y = EstimatedSalary )) +
geom_line()
8. ggplot scatter + line
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
geom_point()
- creating a bar graph
barplot(df$Age, names.arg = df$EstimatedSalary)
10.barplot of AGE
barplot(table(df$Age))
11. barplot of EStimatedSalary
barplot(table(df$EstimatedSalary))
12.bargraph using ggplot
library(ggplot2)
# Bar graph of values. This uses the BOD data frame, with the
# "Time" column for x values and the "demand" column for y values.
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col()
13.Convert the x variable to a factor, so that it is treated as discrete
ggplot(df, aes(x = factor(Purchased), y = EstimatedSalary)) +
geom_col()
- Bar graph of counts
ggplot(df, aes(x = Age)) +
geom_bar()
15.Histogram
hist(df$Age)
- hist of salalry
hist(df$EstimatedSalary)
17. hist of purchased
hist(df$Purchased)
- histogram with ggplot2
library(ggplot2)
ggplot(df, aes(x = Age)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
19.histogram with ggplot2 of Estimated salary
library(ggplot2)
ggplot(df, aes(x = EstimatedSalary)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
20.With wider bins
ggplot(df, aes(x = Age)) +
geom_histogram(binwidth = 4)
- creating a box plot
boxplot(Age ~ EstimatedSalary, data = df)
22.Put interaction of two variables on x-axis
boxplot(Age ~ EstimatedSalary + Purchased, data = df)
23.boxplot using ggplot2
library(ggplot2)
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_boxplot()## Warning: Continuous x aesthetic -- did you forget aes(group=...)?
- boxplot (x interaction)
ggplot(df, aes(x = interaction(EstimatedSalary,Purchased ), y = Age)) +
geom_boxplot()
25. Plotting a Function Curve
curve(x^3 - 5*x, from = -4, to = 4)
26. Plot a user-defined function
myfun <- function(xvar) {
1 / (1 + exp(-xvar + 10))
}
curve(myfun(x), from = 0, to = 20)
# Add a line:
curve(1 - myfun(x), add = TRUE, col = "red")
27.using ggplot2
library(ggplot2)
# This sets the x range from 0 to 20
ggplot(data.frame(x = c(0, 20)), aes(x = x)) +
stat_function(fun = myfun, geom = "line")
chapter 3
library(gcookbook) # Load gcookbook for the pg_mean data set
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col()
29.
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col()
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col(fill = "lightblue", colour = "black")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(position = "dodge")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased)))+
geom_col(position = "dodge", colour = "black") +
scale_fill_brewer(palette = "Pastel1")
33.
ggplot(df, aes(x = Age)) +
geom_bar()
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col()
ggplot(df, aes(x = reorder(Age, EstimatedSalary), y = EstimatedSalary, fill = factor(Purchased))) +
geom_col(colour = "black") +
scale_fill_manual(values = c("#669933", "#FFCC66")) +
xlab("State")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_col(position = "identity", colour = "black", size = 0.25) +
scale_fill_manual(values = c("#CCEEFF", "#FFDDDD"), guide = FALSE)## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
- Adjusting Bar Width and Spacing
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col()
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col(width=0.5)
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_col(width=1) 
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(width = 0.5, position = "dodge")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(width = 0.5, position = position_dodge(0.7))
ggplot(df, aes(x = Age , y = EstimatedSalary, fill = Purchased)) +
geom_col()
43.
ggplot(df, aes(x = Age, y =EstimatedSalary , fill = Purchased)) +
geom_col() +
guides(fill = guide_legend(reverse = TRUE))
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(position = position_stack(reverse = TRUE)) +
guides(fill = guide_legend(reverse = TRUE))
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_col(colour = "black") +
scale_fill_brewer(palette = "Pastel1")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(position = "fill")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(position = "fill") +
scale_y_continuous(labels = scales::percent)
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_col(colour = "black", position = "fill") +
scale_y_continuous(labels = scales::percent) +
scale_fill_brewer(palette = "Pastel1")
49.
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col()
ggplot(df, aes(x = interaction(Age, Purchased), y = EstimatedSalary)) +
geom_col() +
geom_text(aes(label = EstimatedSalary), vjust = 1.5, colour = "white")
ggplot(df, aes(x = interaction(Age, Purchased), y = EstimatedSalary)) +
geom_col() +
geom_text(aes(label = EstimatedSalary), vjust = -0.2)
ggplot(df, aes(x = factor(Purchased))) +
geom_bar() +
geom_text(aes(label = ..count..), stat = "count", vjust = 1.5,
colour = "white")
ggplot(df, aes(x = interaction(Age, Purchased), y = EstimatedSalary)) +
geom_col() +
geom_text(aes(label = EstimatedSalary), vjust = -0.2) +
ylim(0, max(df$EstimatedSalary) * 1.05)## Warning: Removed 279 rows containing missing values (geom_col).
ggplot(df, aes(x = interaction(Age, Purchased), y = EstimatedSalary)) +
geom_col() +
geom_text(aes(y = EstimatedSalary + 0.1, label = EstimatedSalary))
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col(position = "dodge") +
geom_text(
aes(label = EstimatedSalary),
colour = "white", size = 3,
vjust = 1.5, position = position_dodge(.9))
56.
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col() +
geom_text(aes(y = EstimatedSalary, label = EstimatedSalary), vjust = 1.5, colour = "white")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = Purchased)) +
geom_col() +
geom_text(aes(y = EstimatedSalary, label = EstimatedSalary), colour = "white")
ggplot(df, aes(x =Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_col(colour = "black") +
geom_text(aes(y = EstimatedSalary,
label = paste(format(EstimatedSalary, nsmall = 2), "kg")),
size = 4) +
scale_fill_brewer(palette = "Pastel1")
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_point()
ggplot(df, aes(x = Age, y = reorder(Age, EstimatedSalary))) +
geom_point(size = 3) + # Use a larger dot
theme_bw() +
theme(
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_line(colour = "grey60", linetype = "dashed")
)
ggplot(df, aes(x = reorder(Age, EstimatedSalary), y = EstimatedSalary)) +
geom_point(size = 3) + # Use a larger dot
theme_bw() +
theme(
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_line(colour = "grey60", linetype = "dashed"),
axis.text.x = element_text(angle = 60, hjust = 1)
)
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_segment(aes(yend = Age), xend = 0, colour = "grey50") +
geom_point(size = 3, aes(colour = factor(Purchased))) +
scale_colour_brewer(palette = "Set1", limits = c("NL", "AL")) +
theme_bw() +
theme(
panel.grid.major.y = element_blank(), # No horizontal grid lines
legend.position = c(1, 0.55), # Put legend inside plot area
legend.justification = c(1, 0.5)
)## Warning: Removed 400 rows containing missing values (geom_point).
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_segment(aes(yend = Age), xend = 0, colour = "grey50") +
geom_point(size = 3, aes(colour = factor(Purchased))) +
scale_colour_brewer(palette = "Set1", limits = c("NL", "AL"), guide = FALSE) +
theme_bw() +
theme(panel.grid.major.y = element_blank()) +
facet_grid(Purchased ~ ., scales = "free_y", space = "free_y")## Warning: Removed 400 rows containing missing values (geom_point).## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
64.
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line()
df1 <- df # Make a copy of the data
df1$Purchased <- factor(df$Purchased)
ggplot(df, aes(x = Age, y = EstimatedSalary, group = 1)) +
geom_line()
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
ylim(0, max(df$EstimatedSalary))
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
expand_limits(y = 0)
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
geom_point()
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
geom_point()
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
geom_point() +
scale_y_log10()
ggplot(df, aes(x = Age, y = EstimatedSalary, colour = Purchased)) +
geom_line()
72.
ggplot(df, aes(x = Age, y = EstimatedSalary, linetype = factor(Purchased))) +
geom_line()
ggplot(df, aes(x = factor(Purchased), y = Age, colour = EstimatedSalary, group = Age)) +
geom_line()
ggplot(df, aes(x = factor(Purchased), y = Age, colour = Age)) + geom_line()
ggplot(df, aes(x = Purchased, y = EstimatedSalary)) +
geom_line()
ggplot(df, aes(x = Age, y = EstimatedSalary, shape = factor(Purchased))) +
geom_line() +
geom_point(size = 4)
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_line() +
geom_point(size = 4, shape = 21) 
ggplot(df, aes(x = Age, y = EstimatedSalary, shape = factor(Purchased))) +
geom_line(position = position_dodge(0.2)) +
geom_point(position = position_dodge(0.2), size = 4) 
79.
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line(linetype = "dashed", size = 1, colour = "blue")
ggplot(df ,aes(x = Age, y = EstimatedSalary, colour = factor(Purchased))) +
geom_line() +
scale_colour_brewer(palette = "Set1")
81.
ggplot(df, aes(x = Age, y = EstimatedSalary, group = Purchased)) +
geom_line(colour = "darkgreen", size = 1.5)
ggplot(df, aes(x = Age, y = EstimatedSalary, colour = factor(Purchased))) +
geom_line(linetype = "dashed") +
geom_point(shape = 22, size = 3, fill = "white")
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
geom_point(size = 4, shape = 22, colour = "darkred", fill = "pink")
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line() +
geom_point(size = 4, shape = 21, fill = "white")
pd <- position_dodge(0.2)
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_line(position = pd) +
geom_point(shape = 21, size = 3, position = pd) +
scale_fill_manual(values = c("black","white"))
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_area()
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_area(fill = "blue", alpha = .2) +
geom_line()
88.
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_area()
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_area(colour = "black", size = .2, alpha = .4) +
scale_fill_brewer(palette = "Blues")
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased),
order = dplyr::desc(EstimatedSalary))) +
geom_area(colour = NA, alpha = .4) +
scale_fill_brewer(palette = "Blues") +
geom_line(position = "stack", size = .2)
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_area(position = "fill", colour = "black", size = .2, alpha = .4) +
scale_fill_brewer(palette = "Blues")
92.
ggplot(df, aes(x = Age, y = EstimatedSalary, fill = factor(Purchased))) +
geom_area(position = "fill", colour = "black", size = .2, alpha = .4) +
scale_fill_brewer(palette = "Blues") +
scale_y_continuous(labels = scales::percent)
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_line(aes(y = EstimatedSalary - Purchased), colour = "grey50",
linetype = "dotted") +
geom_line(aes(y =EstimatedSalary + Purchased), colour = "grey50",
linetype = "dotted") +
geom_line()
ggplot(df, aes(x = Age, y = EstimatedSalary, shape = factor(Purchased), colour = factor(Purchased))) +
geom_point() +
scale_shape_manual(values = c(1,2)) +
scale_colour_brewer(palette = "Set1")
ggplot(df, aes(x = Age, y = EstimatedSalary, shape = factor(Purchased))) +
geom_point(size = 3) +
scale_shape_manual(values = c(1, 4))
ggplot(df, aes(x = Age, y = EstimatedSalary, shape = factor(Purchased), fill = factor(Purchased))) +
geom_point(size = 2.5) +
scale_shape_manual(values = c(21, 24)) +
scale_fill_manual(
values = c(NA, "black"),
guide = guide_legend(override.aes = list(shape = 21))
)
df3 <- ggplot(df, aes(x = Age, y = EstimatedSalary))
df3 +
geom_point()
df3 +
stat_bin2d(bins = 50) +
scale_fill_gradient(low = "lightblue", high = "red", limits = c(0, 6000))
ggplot(df, aes(x = Age, y = EstimatedSalary)) +
geom_point(
position = position_jitter(width = 0.3, height = 0.06),
alpha = 0.4,
shape = 21,
size = 1.5
) +
stat_smooth(method = glm, method.args = list(family = binomial))## `geom_smooth()` using formula 'y ~ x'## Warning: Computation failed in `stat_smooth()`:
## y values must be 0 <= y <= 1
ggplot(df, aes(x =Age, y = EstimatedSalary)) +
geom_point(
position = position_jitter(width = .3, height = .08),
alpha = 0.4,
shape = 21,
size = 1.5
) +
geom_line(data = df, colour = "#1177FF", size = 1)
##Github Repository https://github.com/akhil-k-m/eda_la/blob/main/la2history.Rhistory